Title
Verification of Safety Rules using NLP
Author
van Gulijk, C.
Holmes, V.
Contributor
Baraldi, P. (editor)
Di Maio, F. (editor)
Zio, E. (editor)
Publication year
2020
Abstract
A key step in the design of digitally enabled safety systems is the development of an Enterprise Architecture model (EA). The design of EA models tends to be a complex job that is usually performed by IT specialists that are not trained in safety. Very often, these EA models contain safety rules that are not well understood by IT specialists but they are of key importance for the safe implementation of the digital solutions. As part of safety directives, safety experts have to verify whether there are any safety issues in the EA model. This particular aspect of enterprise architecture verification is a manual process that is laborious and prone to error. This work investigates whether standard Natural Language Processing techniques (NLP) can help in the verification of safety rules within an enterprise architecture model. This paper demonstrates that this kind of verification is potentially very powerful but cannot be used on its own; ontological taxonomies are probably required as well.
Subject
Work and Employment
Healthy Living
Safety system
Enterprise architecture
Natural language processing
Word2vec
Cosine similarity
Jaccard similarity
To reference this document use:
http://resolver.tudelft.nl/uuid:ee1df2f6-1fef-4f2f-b1c4-b3b2e7349a80
TNO identifier
882007
Publisher
Research Publishing Services, Singapore
Source
30th European Safety and Reliability Conference and 15th Probabilistic Safety Assessment and Management Conference (ESREL2020 PSAM15) 1-6 November Venice, Italy
Document type
conference paper